Imaging is an emerging contact-less high throughput technology employed to retrieve quantitative and qualitative plant traits. In addition, it is often combined with artificial neural networks (ANNs) to further improve the reliability of image-based digital proxies. The olive oil industry is expanding globally as olive oil is increasingly recognized as a functional food. Fast and reliable determination of fruit quality traits is challenging in the agricultural sector. This study summarizes recent advances in the use of RGB-based imaging combined with ANNs to (i) predict oil and phenol concentrations in olive fruit and (ii) classify fruit at harvest according to colour and defects. Opportunities and limitations are also discussed.

Measuring fruit quality traits in olive through RGB imaging and artificial neural networks: opportunities and limitations

Giuseppe Montanaro
;
Francesco Cellini;Antonio Carlomagno;Vitale Nuzzo
2023-01-01

Abstract

Imaging is an emerging contact-less high throughput technology employed to retrieve quantitative and qualitative plant traits. In addition, it is often combined with artificial neural networks (ANNs) to further improve the reliability of image-based digital proxies. The olive oil industry is expanding globally as olive oil is increasingly recognized as a functional food. Fast and reliable determination of fruit quality traits is challenging in the agricultural sector. This study summarizes recent advances in the use of RGB-based imaging combined with ANNs to (i) predict oil and phenol concentrations in olive fruit and (ii) classify fruit at harvest according to colour and defects. Opportunities and limitations are also discussed.
2023
979-8-3503-1271-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/173355
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